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IEEE International Conference on Robotics and Automation (ICRA)

作者： Alexander Schmidt Christian Kotschote Oliver Riedel Institute for Control Engineering of Machine Tools and Manufacturing Units University of Stuttgart Stuttgart Germany AUDI AG Neckarsulm Germany

Due to manufacturing tolerances, the geometry parameters of workpieces are not constant in industrial welding applications. Today, this problem is addressed by either accepting fluctuating part quality or by measuring the geometry and adjusting the configuration of the robot and process controller for each individual part. However, measuring the geometry requires additional manufacturing time or reduces the accessibility of the welding tool. This paper presents an approach for estimating the workpiece geometry based on process data during the welding process, which eliminates the need of measuring the geometry in advance, yet provides the same accessibility of the welding tool. For this approach, a data set is generated by conducting welding experiments with varying part geometries. Welding current, welding voltage and acoustic emissions are recorded. Three supervised learning techniques, which are Support Vector machines, Random Forest Decision Trees and k-Nearest-Neighbors, are used to train models for estimating the geometry. The results show, that the trained models successfully estimate the part geometry with over 90% accuracy.

关键词： Geometry Support vector machines Service robots Welding Supervised learning Time series analysis Estimation

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Orientation smoothing in multi-axis additive manufacturing

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Procedia CIRP 2022年 107卷 357-362页

作者： Maxime Chalvin Frederik Wulle Sébastien Campocasso Anja Elser Vincent Hugel Alexander Verl Université de Toulon COSMER Toulon France Institute for Control Engineering of Machine Tools and Manufacturing Units University of Stuttgart Seidenstraße 36 70174 Stuttgart Germany ARENA2036 e.V. Pfaffenwaldring 19 70569 Stuttgart Germany

In multi-axis processes, such as material extrusion like Fused Filament Fabrication (FFF), the orientation of the tool relatively to the workspace is usually set by the CAM software to an angle that is locally constant relative to the manufacturing surface. However, in the case of large curvature gradients or small curvature radius of the surface or the path, this method leads to significant variations of the tool orientation. These variation require large compensating movements of the axes to maintain the desired deposition tool orientation along the path. Kinematic constraints such as acceleration or jerk limits can lead to a reduction of the path velocity or noticeable positioning errors along the movement, as well as unwante vibrations, which means poor process conditions and defects in the parts. The paper presents a post path planning algorithm that allows smooth in of the tool orientation to avoid jerky compensation motion of the CNC system by varying the orientation within a defined tolerance. This tolerance depends on the deposition process, and is determined experimentally for a FFF process. The proposed methodology was successfully validated b the manufacturing of two sample parts using a robotic additive manufacturing cell.

关键词： Additive Manufacturing Robot machine tool Optimization Tool path

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A System for Reproducible 3D Ultrasound Measurements of Skeletal Muscles

TechRxiv

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TechRxiv 2023年

作者： Sahrmann, Annika S. Handsfield, Geoffrey G. Gizzi, Leonardo Gerlach, Jenin Verl, Alexander Besier, Thor F. Röhrle, Oliver University of Stuttgart Germany The Institute for Modelling and Simulation of Biomechanical Systems University of Stuttgart Stuttgart70569 Germany The Stuttgart Center for Simulation Science SimTech EXC2075 – 390740016 Germany The Auckland Bioengineering Institute Auckland10202 New Zealand The Institute for Control Engineering of Machine Tools Manufacturing Units University of Stuttgart Stuttgart70569 Germany

In 3D freehand ultrasound imaging, operator dependent variations in applied forces and movements can lead to errors in the reconstructed images. In this paper, we introduce an automated 3D ultrasound system, which enables acquisitions with controlled movement trajectories by using motors, which electrically move the probe. Due to integrated encoders there is no need of position sensors. An included force control mechanism ensures a constant contact force to the skin. We conducted 8 trials with the automated 3D ultrasound system on 2 different phantoms with 3 force settings and 10 trials on a human tibialis anterior muscle with 2 force settings. For comparison, we also conducted 8 freehand 3D ultrasound scans from 2 operators (4 force settings) on one phantom and 10 with one operator on the tibialis anterior muscle. Both freehand and automated trials showed small errors in volume and length computations of the reconstructions, however the freehand trials showed larger standard deviations. We also computed the thickness of the phantom and the tibialis anterior muscle. We found significant differences in force settings for the operators and higher coefficients of variation for the freehand trials. Overall, the automated 3D ultrasound system shows a high accuracy in reconstruction. Due to the smaller coefficients of variation, the automated 3D ultrasound system enables more reproducible ultrasound examinations than the freehand scanning. Therefore, the automated 3D ultrasound system is a reliable tool for 3D investigations of skeletal muscle. © 2023, CC BY.

关键词： Phantoms

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Self-adaptive manufacturing with digital twins

arXiv

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arXiv 2021年

作者： Bolender, Tim Bürvenich, Gereon Dalibor, Manuela Rumpe, Bernhard Wortmann, Andreas Software Engineering RWTH Aachen University Aachen Germany Institute for Control Engineering of Machine Tools and Manufacturing Units University of Stuttgart Stuttgart Germany

Digital Twins are part of the vision of Industry 4.0 to represent, control, predict, and optimize the behavior of Cyber-Physical Production Systems (CPPSs). These CPPSs are long-living complex systems deployed to and configured for diverse environments. Due to specific deployment, configuration, wear and tear, or other environmental effects, their behavior might diverge from the intended behavior over time. Properly adapting the configuration of CPPSs then relies on the expertise of human operators. Digital Twins (DTs) that reify this expertise and learn from it to address unforeseen challenges can significantly facilitate self-adaptive manufacturing where experience is very specific and, hence, insufficient to employ deep learning techniques. We leverage the explicit modeling of domain expertise through case-based reasoning to improve the capabilities of Digital Twins for adapting to such situations. To this effect, we present a modeling framework for self-adaptive manufacturing that supports modeling domain-specific cases, describing rules for case similarity and case-based reasoning within a modular Digital Twin. Automatically configuring Digital Twins based on explicitly modeled domain expertise can improve manufacturing times, reduce wastage, and, ultimately, contribute to better sustainable manufacturing. © 2021, CC BY.

关键词： Case based reasoning

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Cloud manufacturing: Theoretical localization within production theory 14

Cloud manufacturing: Theoretical localization within product...

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14th CIRP Conference on Intelligent Computation in Manufacturing engineering, CIRP ICME 2020

作者： Henzel, Robert Ellwein, Carsten Herzwurm, Georg Riedel, Oliver Nobelstr. 12 Stuttgart70569 Germany Institute for Control Engineering of Machine Tools and Manufacturing Units University of Stuttgart Seidenstr. 36 StuttgartD-70174 Germany University of Stuttgart Department of Information Systems II Keplerstr. 17 Stuttgart70174 Germany

The industrial production landscape is significantly affected by digitization. In the course of this development, various production paradigms have been proposed, such as ubiquitous manufacturing, distributed manufacturing or, most recently, cloud manufacturing. A selective discussion and interpretation of these similar concepts remains in the literature as a research gap. Following the call for research, the aim of this paper is to distinguish the concept of cloud manufacturing from neighboring production paradigms and to locate it within the theory. This goal will be achieved by means of a literature analysis as well as argumentation based on it and finally presented graphically. © 2021 The Authors. Published by Elsevier B.V.

关键词： Industrial research

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Implementation of an Intelligent System Architecture for Process Monitoring of machine tools

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Procedia CIRP 2021年 96卷 342-346页

作者： B. Schmucker F. Trautwein T. Semm A. Lechler M.F. Zaeh A. Verl Institute for Machine Tools and Industrial Management (iwb Technical University of Munich) Boltzmannstr. 15 85748 Garching b. München Germany Institute for Control Engineering of Machine Tools and Manufacturing Units (ISW University of Stuttgart) Seidenstr. 36 70174 Stuttgart Germany

State of the art machine tool controllers offer several Internet-of-Things (IoT) interfaces for machine data acquisition using industrial or edge computers. However, the available data exchange rates for these communication platforms are limited to a few hundred Hertz. As the data is not available in high frequency resolution, such a network communication is not suitable for monitoring and optimizing highly dynamic machining processes. This paper describes an efficient system architecture, which enables the acquisition of internal machine data as well as the high frequency sampling of external sensors. Based on this data, an Operational Modal Analysis (OMA) approach can be used to determine the tool tip dynamics during the machining process. Identification of tool tip frequency response requires the reconstruction of the excitation of the machine tool structure, i.e. the occurring machining forces. For this purpose, an approach relying on monitoring the commanded motor currents is applied.

关键词： machine tools Modal Analysis Industry 4.0 Structural Dynamics

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Maturity Evaluation of Domain-Specific Language Ecosystems for Cyber-Physical Production Systems

Maturity Evaluation of Domain-Specific Language Ecosystems f...

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International Conference on Emerging Technologies and Factory Automation (ETFA)

作者： Sandra Greiner Bianca Wiesmayr Kevin Feichtinger Kristof Meixner Marco Konersmann Jérôme Pfeiffer Michael Oberlehner David Schmalzing Andreas Wortmann Bernhard Rumpe Rick Rabiser Alois Zoitl Software Engineering Group University of Bern Switzerland LIT CPS Lab Johannes Kepler University Linz Austria CDL SQI Institute of Information Systems Engineering Technische Universität Wien Austria Software Engineering RWTH Aachen University Germany Institute for Control Engineering of Machine Tools and Manufacturing Units (ISW) University of Stuttgart Germany CDL VaSiCS LIT CPS Lab Johannes Kepler University Linz Austria

engineering Cyber-Physical Production Systems (CPPSs) heavily relies on Domain-Specific Languages (DSLs), which are tailored to a specific class of problems inherent to CPPSs. DSLs enable non-programming experts to solve problems in their domain, such as modeling production processes, implementing control software, or managing the variability of production systems. A DSL ecosystem encompasses the entire infrastructure (e.g., libraries and tools) built around its language and contributes to the successful and easy adoption thereof by domain experts. We present a maturity evaluation model for DSL ecosystems serving two aims: to developers, it reveals missing but essential aspects of the ecosystem, and users (e.g., industrial companies) can evaluate the maturity of a DSL ecosystem. We propose criteria to evaluate the maturity of DSL ecosystems and apply them to existing, publicly available DSLs which have been adopted in CPPSs. The results demonstrate that all components of the model are covered and they allow for deriving hypotheses about DSL ecosystems used in CPPSs.

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A Novel Approach for Unsupervised Sensor Error Classification

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Procedia CIRP 2024年 130卷 206-213页

作者： Samiha Durnagöz Tobias Eberhardt Felix Fuchs Peter Reimann Thomas Ackermann Maik Fiedler Mathias Mayer Marco F. Huber Alexander Verl Graduate School of Excellence advanced Manufacturing Engineering GSaME University of Stuttgart Nobelstraße 12 70569 Stuttgart Germany Institute of Industrial Manufacturing and Management IFF University of Stuttgart Allmandring 35 70569 Stuttgart Germany AUDI AG NSU-Straße 1 74172 Neckarsulm Germany Fraunhofer Institute for Manufacturing Engineering and Automation IPA Nobelstraße 12 70569 Stuttgart Germany Institute for Control Engineering of Machine Tools and Manufacturing Units ISW University of Stuttgart Seidenstraße 36 70174 Stuttgart Germany J. Schmalz GmbH Johannes-Schmalz-Straße 1 72293 Glatten Germany Institute of Parallel and Distributed Systems (IPVS) Universitätsstraße 38 70569 Stuttgart Germany

The precise detection and classification of sensor errors is crucial for the optimal operation of smart manufacturing systems. Sensor errors, which can manifest in various forms, such as drifts or constant values, significantly challenge data integrity. This work introduces a novel approach that combines unsupervised learning and system simulations to detect and classify relevant sensor errors. Our approach avoids extensive manual labeling and uses only error-free simulated time series data to create initial embeddings of sensor data. This establishes a baseline for normal sensor behavior. To facilitate a precise identification of error types through a classifier, simulated sensor errors are introduced to create context-aware embeddings. We further analyze and compare conventional to advanced embedding methods such as TS2Vec. We apply our approach to a synthetic dataset obtained from a vacuum handling system and accurately identify and classify relevant sensor error types. Our work contributes to advancing operational efficiency and reliability in automated manufacturing processes within the Industry 4.0 framework by improving the detection capabilities for sensor errors.

关键词： Unsupervised learning Sensor errors TS2Vec System simulation

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Towards a systematic engineering of industrial domain-specific languages

arXiv

引用

arXiv 2021年

作者： Gupta, Rohit Kranz, Sieglinde Regnat, Nikolaus Rumpe, Bernhard Wortmann, Andreas Siemens AG Munich Germany Software Engineering RWTH Aachen University Aachen Germany Institute for Control Engineering of Machine Tools and Manufacturing Units University of Stuttgart Stuttgart Germany

Domain-Specific Languages (DSLs) help practitioners in contributing solutions to challenges of specific domains. The efficient development of user-friendly DSLs suitable for industrial practitioners with little expertise in modelling still is challenging. For such practitioners, who often do not model on a daily basis, there is a need to foster reduction of repetitive modelling tasks and providing simplified visual representations of DSL parts. For industrial language engineers, there is no methodical support for providing such guidelines or documentation as part of reusable language modules. Previous research either addresses the reuse of languages or guidelines for modelling. For the efficient industrial deployment of DSLs, their combination is essential: the efficient engineering of DSLs from reusable modules that feature integrated documentation and guidelines for industrial practitioners. To solve these challenges, we propose a systematic approach for the industrial engineering of DSLs based on the concept of reusable DSL Building Blocks, which rests on several years of experience in the industrial engineering of DSLs and their deployment to various organizations. We investigated our approach via focus group methods consisting of five participants from industry and research qualitatively. Ultimately, DSL Building Blocks support industrial language engineers in developing better usable DSLs and industrial practitioners in more efficiently achieving their modelling. Copyright © 2021, The Authors. All rights reserved.

关键词： Problem oriented languages

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Fast Verification of Petri Net-Based Model of Industrial Decision-Making Systems: A Case Study

Fast Verification of Petri Net-Based Model of Industrial Dec...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Remigiusz Wiśniewski Justyna Patalas-Maliszewska Marcin Wojnakowski Marcin Topczak Mengchu Zhou Institute of Control and Computation Engineering Univ. of Zielona Góra Zielona Góra Poland Inst. of Mechanical Eng. Univ. of Zielona Góra Zielona Góra Poland Institute for Machine Tools and Production Processes Chemnitz University of Technology Germany Institute of Control and Computation Engineering University of Zielona Góra Zielona Góra Poland The Doctoral School of Exact and Techn. Sciences University of Zielona Góra Zielona Góra Poland ECE Department New Jersey Institute of Technology Newark NJ USA

This work deals with the verification of a decision-making system for additive manufacturing (AM) technology adoption specified by a Petri net. An innovative verification technique of a Petri net-based system is oriented toward practical applications, and can detect errors at the early design and modelling stage. The idea is illustrated by a real-life case study of supporting decision making in AM technology adoption affecting supply chain management (SCM). Two main issues are addressed. Firstly, making optimal decisions about AM technology requires models rarely possessed within a company. Therefore, this work proposes a model supporting the decision making related to the implementation of AM technology, based on a Petri net, by utilizing its main advantages: graphical modelling, and strong mathematical support of formal verification techniques. Contrary to the most popular analysis methods (which are bounded exponentially in a general case), it is proved that the presented method is bounded by a cubic polynomial with net size. Secondly, an investment in AM technology is often financially assessed and does not affect other processes, as in our SCM case. Hence, strong connectivity within the proposed Petri net-based model is examined.

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